Ideal target weight training recommendation system and method

ABSTRACT

An ideal target weight training recommendation system and method ascertains a user&#39;s ideal target weight with a user-data algorithm that computes a strength coefficient based on weight repetitions. The user generates initial completed repetitions until the muscles fatigue. A baseline strength value is calculated with the initial completed repetitions and a baseline strength coefficient. A resistance value of the free weights divided by the baseline strength coefficient, produces the baseline strength value. The baseline strength value is used in calculating ideal target weight values. A user-data defined y-intercept approximate functions. Each y-intercept approximate functions involving user-selected desired target repetition values. The user manipulates a resistance structure associated with the ideal target weight values. The ideal target weight values are rounded to the nearest whole number. The user-selected desired target repetition values generate second completed repetition values for subsequent sets. This causes the strength curve to automatically adapts to strength progression.

FIELD OF THE INVENTION

The present invention relates generally to exercise systems and methods, and, more particularly, relates to a system and method to generate a personalized strength curve for any exercise intended to train any muscle or muscle group and for recommending an ideal target resistance value, i.e., weight or resistance emulating a physical weight, to be employed by the user when performing the exercise.

BACKGROUND OF THE INVENTION

Typically, strength training exercises require manipulation of free weight bars and/or resistance equipment. Exercise regimes utilize resistance devices, such as dumbbells, barbells, resistance bands, isometric exercise devices, strength training machines, and the like. Other strength exercises include pull ups, push-ups, and squats that use body weight to provide resistance force.

Generally, a strength curve is a model that shows how much force you can produce at the joint angles throughout a range of motion for a given strength exercise routine or exercise. However, many known strength curves and workout plans with suggested exercise sets and repetitions (also referred to herein as “rep” or “reps” (plural)) recommendation models fail to indicate or suggest the ideal target weight or resistance to use or to sufficiently or adequately adapt to a wide variety of users over most, if not all, exercise routines.

Therefore, a need exists to overcome the problems with the prior art as discussed above.

SUMMARY OF THE INVENTION

The invention provides an ideal target resistance training recommendation system and method that overcomes the hereinafore-mentioned disadvantages of the heretofore-known devices and methods of this general type and that works for any workout plan or exercise routine. The weight forecasting method ascertains a user's ideal target weight by employing the use of one or more algorithms which define a strength curve (referred to as a “Lynch” curve) that includes a strength coefficient based on the maximum number of repetitions the user achieved until reaching fatigue/failure. Data was gathered from hundreds of actual weight and repetition (also referred to herein as a “rep” or “reps” (plural)) combinations from either male and female individuals with ages ranging from 18 to 65, novices, and athletes in order to generate an initial algorithm for either a male or female user, also referred to herein as a “Lynch Baseline Strength Curve.” The Lynch Baseline Strength Curve has been tested and found to estimate the weight or resistance a user should use to reach the number of repetition he/she desires, with a high degree of accuracy, before failure. Often, the Lynch Baseline Strength Curve is automatically adjusted, enabling the personal characteristics of the user to adjust the curve to accommodate increases/decreases in strength.

The initial completed repetitions using the method, generate with one of a plurality of curve fitting logarithmic, quadratic, and/or power equations, a y-intercept baseline resistance value, i.e., a weight value or resistance value emulating a weight, e.g., through resistance bands. The y-intercept baseline weight or resistance value multiplied by the baseline strength coefficient/factor of the Lynch Baseline Strength Curve generates an ideal target weight value for a plurality of user-selected desired target repetition values.

The system and method progress through three stages of development in order to achieve a unique and personalized strength curve for any muscle or muscle group the user chooses to develop. The personalized strength curve evolves and changes form as the user's strength changes. The curve automatically adapts to the user's strength progression and muscle development.

With the foregoing and other objects in view, there is provided, in accordance with the invention, a weight-implemented method for recommending an ideal number of repetitions and an ideal target weight for a weight-training routine. The method includes an initial Step of providing at least one resistance structure with a weight or resistance value and operably configured to be manipulated, e.g., lifted or pulled, by a user for an exercise routine.

Another Step may include providing a mobile electronic device of the user with a software application resident thereon, the mobile electronic device operably configured to communicatively couple with a server computer over a network, the mobile electronic device of the user and the server computer each having a processor and memory operably coupled thereto.

Yet another Step comprises lifting at least one free weight structure by the user until a muscle fatigue is reached, employing a recommended proper resistance structure use protocol for the exercise routine and ascertaining a number of completed repetitions associated with the weight value.

The method also includes a Step of initiating the software application to generate a user interface on the mobile electronic device.

A Step comprises utilizing the number of initial completed repetitions on the user interface of the software application to generate, with one of a plurality of user-data defined y-intercept approximation functions, a baseline strength coefficient, the weight or resistance value divided by the baseline strength coefficient to generate a baseline strength value.

Another Step for the method may include utilizing the baseline strength value on the user interface of the software application to generate a plurality of ideal target weight values utilizing the one of the plurality of user-data defined y-intercept approximation functions, each involving a plurality of user-selected desired target repetition values.

A final Step comprises lifting by the user, during a plurality of subsequent sets involving the recommended proper weight resistance structure use protocol for the exercise routine, the at least one free weight structure associated with the plurality of ideal target weight values, respectively, that are each rounded to the nearest whole number and for the greater of the plurality of user-selected desired target repetition values or until a muscle fatigue is reached to generate second completed repetition values for each of the plurality of subsequent sets.

In accordance with another feature, an embodiment of the present invention includes an additional Step of plotting the rounded plurality of ideal target weight values with respect to the second completed repetition values for each of the plurality of subsequent sets to generate another of the plurality of user-data defined y-intercept approximation functions dictated by a highest coefficient of determination.

In accordance with a further feature of the present invention, the another of the plurality of user-data defined y-intercept approximation functions consist essentially of a logarithmic function, a quadratic function, and a power function.

In accordance with a further feature of the present invention, the one of a plurality of y-intercept approximation functions is a natural logarithmic function.

In accordance with a further feature of the present invention, the natural logarithmic function is xln(ICR)+b, wherein ICR is the number of initial completed repetitions associated with the weight value.

In accordance with a further feature of the present invention, further comprising a step of selecting, by the user, the weight-training routine and a number of targeted repetitions.

In accordance with a further feature of the present invention, the recommended proper resistance structure use protocol comprises, when utilized with respect to a free weight, a bicep curl motion using a dumbbell for an arm curl, or a 90° extension motion for a leg curl.

In accordance with a further feature of the present invention, the number of initial completed repetitions associated with the weight or resistance value comprises 5 to 20 repetitions.

In accordance with a further feature of the present invention, further comprising a step of utilizing the number of initial completed repetitions, and the weight or resistance value of the free weight structure, on the user interface of the software application to generate, with one of a plurality of user-data defined y-intercept approximation functions, a baseline strength coefficient, the weight or resistance value divided by the baseline strength coefficient to generate a baseline strength value.

In accordance with a further feature of the present invention, further comprising a step of generating a strength curve based on the completed repetitions, and the weight or resistance value of the free weight structure, whereby the repetitions comprise a user-defined x-intercept variable, and the weight values comprise a user-data defined y-intercept variable.

In accordance with a further feature of the present invention, the strength curve comprises a Lynch Strength Curve.

In accordance with a further feature of the present invention, the baseline strength coefficient is generated on the user interface of the software application with at least one of a look up table and a completed repetition input field.

Although the invention is illustrated and described herein as embodied in a ideal target weight training recommendation system and method, it is, nevertheless, not intended to be limited to the details shown because various modifications and structural changes may be made therein without departing from the spirit of the invention and within the scope and range of equivalents of the claims. Additionally, well-known elements of exemplary embodiments of the invention will not be described in detail or will be omitted so as not to obscure the relevant details of the invention.

Other features that are considered as characteristic for the invention are set forth in the appended claims. As required, detailed embodiments of the present invention are disclosed herein; however, it is to be understood that the disclosed embodiments are merely exemplary of the invention, which can be embodied in various forms. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one of ordinary skill in the art to variously employ the present invention in virtually any appropriately detailed structure. Further, the terms and phrases used herein are not intended to be limiting; but rather, to provide an understandable description of the invention. While the specification concludes with claims defining the features of the invention that are regarded as novel, it is believed that the invention will be better understood from a consideration of the following description in conjunction with the drawing figures, in which like reference numerals are carried forward. The figures of the drawings are not drawn to scale.

Before the present invention is disclosed and described, it is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. The terms “a” or “an,” as used herein, are defined as one or more than one. The term “plurality,” as used herein, is defined as two or more than two. The term “another,” as used herein, is defined as at least a second or more. The terms “including” and/or “having,” as used herein, are defined as comprising (i.e., open language). The term “coupled,” as used herein, is defined as connected, although not necessarily directly, and not necessarily mechanically. The term “providing” is defined herein in its broadest sense, e.g., bringing/coming into physical existence, making available, and/or supplying to someone or something, in whole or in multiple parts at once or over a period of time. Also, for purposes of description herein, the terms “upper”, “lower”, “left,” “rear,” “right,” “front,” “vertical,” “horizontal,” and derivatives thereof relate to the invention as oriented in the figures and is not to be construed as limiting any feature to be a particular orientation, as said orientation may be changed based on the user's perspective of the device. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary or the following detailed description.

As used herein, the terms “about” or “approximately” apply to all numeric values, whether or not explicitly indicated. These terms generally refer to a range of numbers that one of skill in the art would consider equivalent to the recited values (i.e., having the same function or result). In many instances these terms may include numbers that are rounded to the nearest significant figure. In this document, the term “longitudinal” should be understood to mean in a direction corresponding to an elongated direction of the free weight structure. The terms “program,” “software application,” and the like as used herein, are defined as a sequence of instructions designed for execution on a computer system. A “program,” “computer program,” or “software application” may include a subroutine, a function, a procedure, an object method, an object implementation, an executable application, an applet, a servlet, a source code, an object code, a shared library/dynamic load library and/or other sequence of instructions designed for execution on a computer system.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying figures, where like reference numerals refer to identical or functionally similar elements throughout the separate views and which together with the detailed description below are incorporated in and form part of the specification, serve to further illustrate various embodiments and explain various principles and advantages all in accordance with the present invention.

FIG. 1 is a block diagram of an exemplary weight-implemented system for recommending an ideal number of repetitions and an ideal target weight for a weight-training routine, in accordance with the present invention;

FIG. 2 is an exemplary look up table, defined by a repetitions value and a baseline factor, in accordance with the present invention;

FIG. 3 is an exemplary look up table, defined by a repetitions value, a baseline factor, and a weight or resistance value for a free weight structure, in accordance with the present invention;

FIG. 4 is an exemplary look up table, referencing actual data points that represent the practical application of weight values during the weight training exercise, in accordance with the present invention;

FIG. 5 is an exemplary strength curve graph with a Lynch Strength Curve, in accordance with the present invention;

FIG. 6 is the look up table for the strength curve graph shown in FIG. 5, in accordance with the present invention;

FIG. 7 is a first strength curve graph, showing the effects of changes in strength on projected and actual repetitions, in accordance with the present invention;

FIG. 8 is a second strength curve graph, showing the effects of changes in strength on projected and actual repetitions, in accordance with the present invention;

FIG. 9 is a strength curve chart that shows causes of differences between projected and actual reps achieved, in accordance with the present invention;

FIG. 10 is a first Lynch Baseline Strength Curve projecting a Target Rep Factor and an Actual Rep Factor, in accordance with the present invention;

FIG. 11 is a second Lynch Baseline Strength Curve projecting a Target Rep Factor and an Actual Rep Factor, in accordance with the present invention;

FIG. 12 is a third Lynch Baseline Strength Curve projecting a Target Rep Factor and an Actual Rep Factor, in accordance with the present invention;

FIG. 13 is a strength curve graph having six or more adjusted new rep datapoints utilized to design a Personalized or Group Specific Strength Curve, in accordance with the present invention; and

FIG. 14 is a flowchart diagram of an exemplary weight-implemented method for recommending an ideal number of repetitions and an ideal target weight for a weight-training routine, in accordance with the present invention.

DETAILED DESCRIPTION

While the specification concludes with claims defining the features of the invention that are regarded as novel, it is believed that the invention will be better understood from a consideration of the following description in conjunction with the drawing figures, in which like reference numerals are carried forward. It is to be understood that the disclosed embodiments are merely exemplary of the invention, which can be embodied in various forms.

The present invention provides a novel and efficient ideal target weight training recommendation system 100 and method 1400. Embodiments of the invention work to ascertain a user's ideal target weight by employing the use of one or more user-data defined (also referred to as “user-defined”) algorithms that includes a strength coefficient based on weight repetitions until a user's muscles reach fatigue.

In additional embodiments, the ideal target weight training recommendation system 100 and method 1400 progresses through three stages of development in order to achieve a unique and personalized strength curve for any muscle or muscle group the user chooses to develop. The personalized strength curve evolves and changes form as the user's strength changes. This continual adjustment of the user's exercise strength curve is critical in order to quickly adjust to increases, or decreases in the muscle's strength, thus permitting the user to maximize the result of his/her workouts.

Referring now to FIG. 1, one embodiment of the system 100 is shown in a block diagram view. FIG. 1 shows several advantageous features of the present invention, but, as will be described below, the invention can be provided in several shapes, sizes, combinations of features and components, and varying numbers and functions of the components. The first example of the system 100 and 1400 includes a free weight structure 106 or resistance mechanism; a mobile electronic device 110 with a software application 124 resident thereon communicatively coupled with a server computer 112 over a network; and a look-up table 114 (LUT) that references a number of repetitions 104 (reps) and a baseline factor 118.

An algorithm provides a formula that process the data points for the reps 104 and baseline factor to derive a preliminary weigh value 108 of the free weight structure 106. A user 102, which may include a person performing weight training exercises, operates the free weight structure 106 based on calculated preliminary weigh values 108 and reps 104. As the user's 102 strength increases/decreases, the system 100 recalculates the preliminary weigh value 108. A subsequent weigh value 108 and reps 104 are calculated for use during exercise. In this manner, the system 100 produces an evolving, customized weight training program for the user 102.

The user 102 initially determines an initial exercise. The exercise may include, without limitation, a bicep curl or a leg curl with a 90° motion, performed on a free weight structure 106 or resistance mechanism. The user 102 then selects a preliminary weigh value 108 for the free weight structure 106. The user 102 performs as many reps 104 as possible with the weigh value 108 until fatigue sets in, and it is not possible to continue.

Additionally, as referenced in FIG. 1, the system 100 utilizes a look-up table (LUT) 114. The LUT 114 is defined by columns containing data for reps 116, a base line factor 118, and a derived preliminary weigh value 120. The LUT 114 is used as a reference to determine a first approximation of a Y-intercept of the combination of weigh values 108 and reps 104. Based on the Y-intercept value, the LUT 114 projects the value of the preliminary weight or resistance value 108 for use in subsequent test sets of the weight training exercise.

Once the weigh value 108 is derived from the reps 116 and base line factor 118 referenced in the LUT 114, the user 102 commences with the weight training exercise, performing as many reps 104 as possible until failure or fatigue prevents continuing the repetitions. During this exercise, the user 102 exercises with a weigh value 108 of the free weight structure 106 derived from the LUT 114. The user 102 performs reps 104 with the weigh value 108 until fatigue sets in, and it is not possible to continue with the reps 104. These actual reps 104 and weigh value 108 may be different than the initial reps 104 and weigh value 108 found in the LUT 114.

As a result of the weight exercises with the initial reps 104 and weigh value 108, the user 102 generates and records an actual rep 104 and weigh value 108. The actual reps 104 represent the reps 104 performed until fatigue does not allow the user 102 to continue with the initial weigh value 108 and reps 104. The user 102 uploads the actual reps 104 and weigh value 108 through the software application 124 for input into an algorithm. The system provides an algorithm that calculates a strength curve that factors in a baseline for the selected weight training exercise and the number of reps performing the weight resistance motion.

This creates an initial muscle strength baseline for the muscle to be trained. The muscle strength baseline is graphically referenced as a strength curve (see FIG. 5). The strength curve may include a model that shows how much force can be produced at the joint angles throughout a range of motion, i.e., a bicep exercise, a leg curl exercise up to 90°. In one non-limiting embodiment of the present invention, the strength curve comprises a Lynch Strength Curve known in the art.

The weigh value 108 and the number of repetitions is entered into the algorithm by uploading into the software application 124 on a mobile electronic device 110. For example, the user 102 selects 25 pounds, which is a weigh value 108 or resistance where 5 to 20 repetitions can be achieved using proper form, until reaching failure. The user 102 then executes as many reps 104 possible until reaching failure. If for example, the user 102 performs 15 reps 104, a weigh value 108 of 25 pounds at 15 reps 104 is performed and entered into the algorithm.

Additionally, user 102 information is uploaded into the algorithm. This additional information creates a database of user information. Exemplary user information may include, without limitation: Identification of the user and user information; Name of the exercise; Date and time each exercise set was initiated and ended, and the rest period between sets; Number of sets performed; Weights used in each set; and Reps achieved in each set. In one non-limiting embodiment, the system 100 provides an Administrator to regulate the uploaded data and the calculations performed by the algorithm.

The system 100 generates an LUT 200, shown in FIG. 2, which is derived from the Lynch Strength Curve baseline algorithm. The LUT 200 has columns for reps 202 and base line factors 204. The data 202, 204 in the LUT 200 is used to determine the first approximation of the user's Y-intercept of the combination of 25 lbs./15 Reps. Based on this value, an LUT 300 is generate. The LUT 300 also has columns for reps 302 and base line factors 304. Additionally, LUT 300 projects multiple preliminary weight or resistance value 306 to be used in the next four test sets of the weight training exercise (see FIG. 3). The LUT 300 provides the user with the weight or resistance value to achieve approximately 5, 10, 15 and 20 or 25 reps.

The algorithm calculates a Y-intercept as follows: 15 Reps Achieved=Factor: 0.523 (based on LUT 200) 25 lbs. Chosen Weight/0.523=47.8 lbs.=Y-intercept

The system 100 applies the Y-intercept value of 47.8 lbs. to the Reps Factors in the Lynch Strength Curve LUT 300 to determine the weight values and targeted reps for the next four sets of the weight training exercise.

For example, as LUT 300 references in FIG. 3, extracted weights for 5, 10, 15, 20 and 25 reps are as follows:

5 Target Reps: 36 lbs.

10 Target Reps: 31 lbs.

15 Target Reps: 25 lbs.

20 Target Reps: 20 lbs.

25 Target Reps: 17 lbs.

This continual adjustment of the user's exercise strength curve is critical in order to quickly adjust to increase or decrease in the muscle's strength. The calculations by the algorithm enable optimal strength progression and muscle development; thereby allowing the user to maximize the result of weightlifting workouts.

It is significant to note that since the exact weights determined by the Lynch Strength Curve LUT 300 may not be available in the location the user is training, the available weight/resistance for the user may require adjusting by rounding up or down to the nearest next weight available. For example, on the first set test set the user achieved 15 Reps, the user performs sets targeting 5, 10, 20 and 25 Reps with the following weights:

5 Target Reps: 35 lbs.

10 Target Reps: 30 lbs.

15 Target Reps: 25 lbs.

20 Target Reps: 20 lbs.

25 Target Reps: 15 lbs.

At this point, the user commences exercising with these data points for reps and weight values. The user executes as many reps possible with the indicated weights until reaching failure for each of the four sets. For example, the actual reps achieved include:

For 35 lbs., 7 reps actually achieved;

For 30 lbs., 13 reps actually achieved;

For 20 lbs, 18 reps actually achieved; and

For 15 lbs, 28 reps actually achieved.

The user may then upload the actual reps 402 and actual weight values 404 into the software application, using the mobile electronic device. The actual data points represent the practical application of weight values during the weight training exercise and are recorded in an LUT 400 for further processing (see FIG. 4).

In one embodiment, an Administrator downloads the user's 102 exercise specific LUT to the user's software application on the mobile electronic device 110. The next time the user 102 trains this exercise this LUT will be the source to determine the weight to use for the exercise based on the number of targeted reps the user and the workout plan recommends. Once each set is completed, the user executes as many reps possible with the indicated weights until reaching failure for each of the four sets. The user 102 also uploads the actual reps and weight values performed into the software application after each set. Thus, with the actual reps and weight or resistance value uploaded, the algorithm calculates a second approximation of a Y-intercept. The second approximation is an improvement on the first approximation, since the actual reps possible until fatigue are being used.

In one possible embodiment of the calculation manifested in a strength curve graph 500, the subsequent calculation involves a quadratic, power or logarithmic equation 504, whichever achieves the highest R squared for each exercise using the datapoints of weight or resistance value and reps uploaded from the software application 124. An exemplary equation 504 is y=0.0252x²−1.89x+47.79 R²=0.9565. The Y-intercept value of X=1 in the polynomial, power or logarithmic equation 504 is then multiplied by the factors for each of the 1 to 30 repetition values in the Lynch Baseline Strength Curve LUT 500 to determine the second approximation of the amount of weight to use in order to achieve the desired number of repetitions before reaching failure. The strength curve 502 and data points 506 a-n for these calculations is referenced in FIG. 5.

The second approximation of the weight or resistance value to be used is downloaded back to the mobile electronic device for weight training use. In this manner, the strength curve is constantly adjusted to factor in increases/decreases in strength. As the weight training exercise is performed and the user's strength increases, the updated weight or resistance value and reps data is uploaded to the user's database. The algorithm continues recalculating the Y-intercept value of X=1 in the polynomial, power or logarithmic equation, whichever returns the highest R squared. And a new exercise specific LUT 600 is generated and downloaded to the mobile electronic device for weight training use (see FIG. 6). The new exercise specific LUT 600 displays the updated reps and weight or resistance value that the user can incorporate into the weight lifting exercise.

Turning now to FIG. 7, the system 100 provides a strength curve graph 700 graphically illustrating progress of the weightlifting workout. As the user's strength increases, the user can perform more repetitions with the same weight value. For example, as shown in graph 700, when the user began training, the user performed 5 Reps with 39 lbs. (A). As strength increased from the workouts, using the same 39 lb. weight or resistance value the user, on average over a number of workouts, can perform 7 repetitions before reaching failure (B). This change in strength moved the user's X, Y datapoint coordinates for this exercise down and to the right on the strength (C) on a Lynch Strength Curve 702.

FIG. 8 shows a curve strength graph 800 as a result of the user's ability to increase from 5 Reps with 39 lbs. (A) to 7 Reps with the same 39 lbs. (B) the X, Y datapoint on a Lynch Strength Curve 802 moved down and to the right from Factor 0.844 to Factor 0.775. Based on this new data, the user's Y-intercept increased from 45.9 lbs. to 50.3 lbs. (39 lbs./0.775=50.3 lbs.), a 9.6% increase in strength. The new x=1, Y-intercept will now be used to calculate the new LUT and from it the appropriate heavier weight to be used the next time this exercise is performed.

Consequently, after five workout sessions of the weight training exercise are completed, the oldest datapoints related to the first workout, are discarded from the formula and replaced with the next, fifth workout datapoints. This continued cycle will ensure the data used in each new weight projection is updated to reflect the user's strength development.

The system 100 further includes a process of building a muscle strength database for each exercise using the strength curve. As the user continues to train and upload the results, the number of datapoints for each weight training exercise in the database increases. As the datapoints for each repetition number (X axis of the graph) reaches 12 or more, the user's individual physiology or strength related to one or more muscle groups differs from the baseline strength curve.

Such a difference can result from injury, a congenital condition, fast or low twitch muscle fiber composition, or existing superior physical conditioning found in advanced or professional athletes amongst many other variables. Thus, every time an individual user's performance diverges from the strength curve, the user's data is used to regenerate a customized strength curve for that specific condition.

The system 100 further includes a process of customizing the strength curve for an individual or a group. FIG. 9 illustrates a strength curve chart 900 that shows causes 904 a-n of differences between projected and actual reps achieved. Once twelve or more datapoints have been registered for targeted repetitions in the 1 to 30 range for a user's given exercise, any significant deviations from a strength curve baseline 902 are identified. The deviations between projected and actual reps achieved are used by the algorithm to calculate the next LUT and strength curve that adapts to the user's increasing/decreasing strength.

It is significant to note that the causes of possible deviations are numerous, with some of the principle ones are described in the graph 900. For example, in a first cause 904 a of differences between projected and actual reps achieved; when the user trains for Hypertrophy with reps in the lower range, over time the training will show the user can perform the same amount of reps with more reps using the same weight value. This causes the higher end of the Lynch Strength Curve to move downward, registering a lower strength utilization factor on the Y-intercept. Further, periodic personalization of the algorithm adjusts for this specific cause of differences.

Once significant deviations are identified, the system 100 excludes the datapoints beyond one sample standard deviation from the Mean of the datapoints related to specific repetition numbers and recalculate the new Mean for that number of repetitions. This action ensures that statistically non-representative datapoints, (possibly registered incorrectly by the user, or being the result of when he/she did not continue repetitions until failure), do not adversely influence the calculation of a new Mean. Performing this process for five or more rep values (example: 5, 10, 15, 20 and 25 repetitions) enables a unique polynomial, power or logarithmic equation to be created for an individual or a specific group of individuals. As a result, a new LUT determines the proper weights to be used in performing that exercise.

Those skilled in the art will recognize that numerous personal factors on any given day can come to bear on a user performing reps, causing an increase or decrease on that training day, in the average maximum number of reps he/she performs until reaching failure. Stress, lack of sleep, a pulled muscle, a bad night sleep, improper nutrition, alcohol, bad form, too much partying or just a lack of enthusiasm to push for those last few reps. On the other hand, a user may be experiencing a great day, properly hydrated, motivated and ready to beat his record and exceed the average number of reps he/she performs for a given exercise.

Furthermore, it is understood in the art that the physiological makeup of a user may be determined by genetics, or prior physical condition and can change his/her ability to perform certain exercises. Age, sex, injuries, muscle composition, physical size, hormone levels, nutrition and many more characteristic make each person a unique individual, with no other alike in the world. No two people are physiologically alike, and no individual is the same from one day to the next. Strength training adds an additional variable that causes continual change in the body's ability to overcome resistance.

Those skilled in the art will recognize that the Lynch Baseline Strength Curve data was gathered from hundreds of actual weight and rep combinations from male and female individuals with ages ranging from 18 to 65, novices and athletes and has been tested and found to estimate the weight or resistance a user should use to reach the number of repetition he/she desires, with a high degree of accuracy. But, as everyone is different and his/her body is continually undergoing change, the Lynch Baseline Strength Curve and the system 100 used to automatically adjust the curve, enable the personal characteristics of an individual or a group to adjust the curve to their reality.

To exemplify how Lynch Strength Curve and its Functions are adjusted to an individual or groups' actual performance, 15 datapoints for three Target Rep's levels will be used; 5, 15 and 20 reps. Normally this process is uses five or more Target Rep levels, but for the purpose of simplification we will use only three. The new Target Reps and their respective Factors derived from the process below will be applied to a polynomial, power or logarithmic equation, or combinations thereof, to generate a personal strength curve for the user

The testing system uses the actual number of reps achieved for one specific exercise by the user before reaching failure and the weight he/she used. For example, as referenced in strength curve graph 1000 of FIG. 10, in the case where the Five Rep Target was tested, the Lynch Baseline Strength Curve projected a Target Rep Factor 1002 of 0.756. The actual reps achieved varied each time the user performed the exercise, as can be seen in the Actual Reps column, or generated second completed repetition values 1006 in table 1004.

The Target Rep Factors 1008 related to the Actual Reps achieved are found in the Actual Rep Factor column. The arithmetic mean of the Target Rep Factors 1002 is 0.756, while the arithmetic mean of the Actual Rep Factor 1008 is 0.762; a small 4/10 of one percent difference indicating that on average the user's strength for repetitions in the 5 repetitions range are just slightly less than projected by the Lynch Baseline Strength Curve.

When the data from the table is graphed in graph 1000, the very small difference between what the Lynch Baseline Strength Curve projected 1002 and the Mean of the Actual Rep Factors 1008 is minute. The actual datapoints are blue. However small the difference, this new Factor for 5 Target Reps is utilized to develop a personalized strength curve algorithm and LUT for the user.

Turning now to FIG. 11, the Fifteen Rep Target is tested. Here, the Lynch Baseline Strength Curve projects a Target Rep Factor 1102 of 0.523. The actual reps achieved varied each time the user performed the exercise, as can be seen in the Actual Reps column in table 1106. The Target Rep Factors 1102 related to the Actual Reps achieved are found in the Actual Rep Factor column 1108. As graph 1100 references, the arithmetic Mean of the Target Rep Factors 1102 is 0.523, while the arithmetic mean of the Actual Rep Factor 1104 is 0.537; a small 2.7 percent difference indicating that the user's strength for repetitions in the 15 repetitions range are slightly less than projected by the Lynch Baseline Strength Curve.

When this data from the table is graphed, the very small difference between what the mean factor the Lynch Baseline Strength Curve projected 1102 and the mean of the Actual Rep Factors 1104 is visible. The actual datapoints are blue. However small the difference, the new Factor for 15 Target Reps is used in developing a personalized strength curve algorithm and LUT for the user.

Looking at FIG. 12, in the case where the Twenty Rep Target are tested, the Lynch Baseline Strength Curve, shown in graph 1200, projects a Target Rep Factor 1202 of 0.407. The Target Rep Factors related to the Actual Reps achieved are found in the Actual Rep Factor column 1208. The arithmetic mean of the Target Rep Factors 1202 is 0.407, while the arithmetic mean of the Actual Rep Factor 1204 is 0.455; an 11.8 percent difference indicating that the user's strength for repetitions in the 20 rep range are somewhat lower than the Lynch Baseline Strength Curve predicted, but still within the actual range of the datapoints.

When this data from table 1206 is graphed, the difference between what the mean factor the Lynch Baseline Strength Curve projected 1202 and the mean of the Actual Rep Factors 1204 can be seen. The actual datapoints are blue. The mean of the Actual Rep Factors for 20 target reps are utilized used in developing a personalized strength curve algorithm and LUT for the user.

As shown in FIG. 13, the system 100 is also useful for comparing the Lynch Baseline Strength Curve to a new personalized or group specific strength curve. The strength curve graph 1300 illustrates six or more adjusted new rep datapoints 1306 a-n are utilized to design a Personalized or Group Specific Strength Curve. In this example, for simplicity sake, the only data graphed is the data for the 1, 5, 15 and 20 repetition levels based on the aforementioned information. Curve 1302 on the graph 1300 is the Lynch Baseline Strength Curve 1304. The strength curve 1304 is a new Personalized or Group Specific Strength Curve.

In this situation, the algorithm which best fit the new data is a logarithmic curve with an R squared of 0.9885 based only of these four datapoints. With three more datapoints (especially for the 8, 12 and 25 rep values) which the new curve reduces the variance and generates a higher R squared. Interpreting the new curve 1304, when compared to the Lynch Baseline Strength Curve 1302, one could conclude that the new curve relates to a person or group training more for hypertrophy than for Endurance. At the low repetition range the new curve 1304 indicates that the user requires less strength on average to achieve up to 6 repetitions. Beyond 6 repetitions the user requires more strength than projected by the Lynch Baseline Strength Curve, on average, to achieve the higher repetition range.

A new LUT is then calculated by applying the new logarithmic curve and its respective Factor values on the Y axis and the related repetition numbers on the X axis. The LUT will be downloaded to the mobile electronic device and used to determine the weights to be used for the weight training exercise until any additional adjustment is made necessary in light of changes in the user performance.

FIG. 14 references a flowchart of an exemplary efficient ideal target weight training recommendation method 1400. FIGS. 1-13 will be described in conjunction with the process flow chart of FIG. 14. Although FIG. 14 shows a specific order of executing the process steps, the order of executing the steps may be changed relative to the order shown in certain embodiments. Also, two or more blocks shown in succession may be executed concurrently or with partial concurrence in some embodiments. Certain steps may also be omitted in FIG. 14 for the sake of brevity. In some embodiments, some or all of the process steps included in FIG. 14 can be combined into a single process.

The method 1400 has the objective of designing a personalized strength curve for any muscle or muscle groups the user 102 wants to develop (see FIG. 5). The strength curve 502 is used to project the weight or resistance to be used in a strength training exercise to achieve a desired number of targeted repetitions per set, at which point the muscle or muscle group will reach failure. Further, the method 1400 is operable with any weight or resistance exercise or workout plan.

For ideal results the method 1400 requires the user 102 to continue preforming repetitions of the exercise until reaching failure. The method 1400 progresses through three stages of development in order to achieve a unique and personalized strength curve for any muscle or muscle group the user chooses to develop. The personalized strength curve evolves and changes form as the user's strength changes. The curve automatically adapts to the user's strength progression and muscle development.

The method 1400 may include an initial Step 1402 of providing at least one free weight structure 106 with a weight or resistance value 108 and operably configured to be lifted by a user 102 for an exercise routine. Another Step 1404 comprises, selecting, by the user, the weight-training routine and a number of targeted repetitions 116. The weight-training routine may include, without limitation, a bicep curl motion using a dumbbell for an arm curl, or a 90° extension motion for a leg curl.

The method 1400 may further comprise a Step 1406 of providing a mobile electronic device 110 of the user with a software application 124 resident thereon, the mobile electronic device operably configured to communicatively couple with a server computer over a network, the mobile electronic device of the user and the server computer each having a processor and memory operably coupled thereto. The mobile electronic device 110 may include, without limitation, a smart phone, a tablet, a laptop, and a processor.

A Step 1408 includes lifting the at least one free weight structure 106 by the user until a muscle fatigue is reached employing a recommended proper resistance structure use protocol 122 (RPRSUP) for the exercise routine and ascertaining a number of initial completed repetitions 116 associated with the weight or resistance value 120. The number of initial completed repetitions comprises 5 to 20 reps. For example, 5, 10, 15, and 20 reps can be performed to ascertain the weight value.

In one non-limiting embodiment, the RPRSUP 122 comprises a bicep curl motion using a dumbbell for an arm curl, or a 90° extension motion for a leg curl. However, in other embodiments, other types of weight or resistance exercises may be used with the present method 1400. In some embodiments, a Step 1410 comprises initiating the software application 124 to generate a user interface on the mobile electronic device 110.

A Step 1412 includes utilizing the number of initial completed repetitions on the user interface of the software application to generate, with one of a plurality of user-data defined y-intercept approximation functions 1010, a baseline strength coefficient 118, the weight or resistance value divided by the baseline strength coefficient 118 to generate a baseline strength value 1016. The user interface allows the user 102 to build a muscle strength database for each exercise using the strength curve. As the user continues to train and upload the results, the number of datapoints for each weight training exercise in the database increases. As the datapoints for each repetition number (X axis of the graph) reaches 12 or more, the user's individual physiology or strength related to one or more muscle groups differs from the baseline strength curve.

In some embodiments, user-data defined y-intercept approximation functions 1010 may include, without limitation, a logarithmic function, a quadratic function, and a power function. In other embodiments, the y-intercept approximation functions 1010 is a natural logarithmic function. In one non-limiting embodiment, the natural logarithmic function is xln(ICR)+b, wherein ICR is the number of initial completed repetitions associated with the weight value. In another embodiment, the baseline strength coefficient 118 is a baseline factor generated on the user interface of the software application with at least one of a LUT 114 and a completed repetition input field 400.

In some embodiments, a Step 1414 may include utilizing the baseline strength value 1016 on the user interface of the software application to generate a plurality of ideal target weight values 1014 utilizing the one of the plurality of user-data defined y-intercept approximation functions, each involving a plurality of user-selected desired target repetition values 1012. This includes a first approximation of the user's Y-intercept of the combination of 25 lbs./15 Reps and based on this value, projects preliminary weights to be used in the next four test sets of the exercise. An LUT derived from the Lynch Strength Curve baseline algorithm determines this first approximation. The LUT provides the user with the weight to achieve approximately 5, 10, 15 and 20 or 25 reps

Another Step 1416 comprises generating a strength curve based on the completed repetitions, and the weight or resistance value of the free weight structure, whereby the repetitions comprise a user-defined x-intercept variable, and the weight values comprise a user-data defined y-intercept variable. The strength curve 502 is used to project the weight or resistance to be used in a strength training exercise to achieve a desired number of targeted repetitions per set, at which point the muscle or muscle group will reach failure. An exemplary LUT 600, and accompanying strength curve graph 500 are illustrated in FIGS. 5-6.

A final Step 1418 comprises lifting by the user, during a plurality of subsequent sets involving the RPRSUP for the exercise routine, the at least one free weight structure associated with the plurality of ideal target weight values 1014, respectively, that are each rounded to the nearest whole number and for the greater of the plurality of user-selected desired target repetition values or until a muscle fatigue is reached to generate second completed repetition values 1006 for each of the plurality of subsequent sets. The second completed repetition values 1006 are a second approximation of the amount of weight to use in order to achieve the desired number of reps before reaching failure.

To achieve the second completed repetition values 1006, the algorithm calculates a quadratic, power or logarithmic equation, whichever achieves the highest R squared for each exercise using the datapoints of weight and reps uploaded from the Users mobile app. The Y-intercept value of X=1 in the polynomial, power or logarithmic equation is then multiplied by the factors for each of the 1 to 30 rep values in the Lynch Baseline Strength Curve LUT to determine the second approximation of the amount of weight to use in order to achieve the desired number of reps before reaching failure. The one of a plurality of y-intercept approximation functions is a natural logarithmic function. In one embodiment, the natural logarithmic function is xln(ICR)+b, wherein ICR is the number of initial completed repetitions associated with the weight value.

Another possible Step 1420 provided by the method 1400 comprises plotting the rounded plurality of ideal target weight values 1014 with respect to the second completed repetition values 1006 for each of the plurality of subsequent sets to generate another of the plurality of user-data defined y-intercept approximation functions 1010 dictated by a highest coefficient of determination.

In operation of the system 100 and method 1400, the user defines a user profile. The user profile includes:

Sex: Female

Age: 21

Level of present level of strength training activity:

1) Novice

2) Less than one year training

3) 1 to 3 years training

4) 3 to 5 years training

5) Over 5 years training

The user may then choose a workout routine. This system and method can be applied to any exercise in any existing workout routine; endurance, strength hypotrophy, physical therapy, etc. In this example, the user's chosen exercise routine starts with a Dumbbell Curl to train the Biceps Brachii muscle and involves four sets of 20, 5, 15 and 10 repetitions.

Muscle to Train: Biceps Brachii

Exercise: Dumbbell Curl

Instruct the user on proper form to execute the exercise

Set 1 Target Reps: 20

Set 2 Target Reps: 5

Set 3 Target Reps: 15

Set 4 Target Reps: 10

The user's Baseline Strength Curve Algorithm is defined. Based on User Profile, determine which of the systems Baseline Strength Curve Algorithm to apply to the person's Workout Routine. In this example the user is a female, less than 30 years old and a novice to strength training, so the BSCA which will be applied to her training routine is: Female/less than 30 Years/Novice: BSCA=−0.18 ln(CR)+1.0212

However, if the user was a male, less than 30 years old and a novice to strength training, the BSCA which would be applied to his training routine would have been: Male/less than 30 Years/Novice: BSCA=−0.17 ln(CR)+0.9602

The user defines a Baseline Strength Coefficient (in this example, Dumbbell Curl). The first time a user chooses to use a new exercise in his/her workout routine he/she must execute a test set of reps to define his/her Baseline Strength Coefficient (BSC) for that exercise. This is done by picking a weight he/she is comfortable in using to achieve between 5 and 15 repetitions. Using proper form, the user performs as many repetitions with the chosen weight until the muscle reaches total fatigue (until the next repetition cannot be completed).

The female user in this example completed 15 Repetitions (CR) with a Weight of 15 lb. weight (W) until total fatigue set in. Her Baseline Strength Coefficient for the set of reps is therefore 0.5338 BSC=−0.18 ln(CR)+1.0212 BSC=(−0.18 ln(15)+1.0212)=0.5338

The user defines a Baseline Strength Value for Exercise 1 BSV=W/BSC BSC=15/0.5338=28.103

An Ideal Target Weight (ITW) for Exercise 1 Set 1 which has a Target Repetition (TR) of 20 Reps is determined: ITW=BSC×(−0.18 ln(TR)+1.0212) ITW=(15/0.5338×(−0.18 ln(20)+1.0212) ITW=28.103×0.4820=13.545 lbs.

To perform the first set of first set of 20 Dumbbell Curl's, the user would ideally use a weight of 12.416 Lbs. As weights don't come in such minor weight increments, this ITW will be adjusted, as explained further in this example.

The user determines the Ideal Target Weight (ITW) for Exercise 1 Set 2 which has a Target Repetition (TR) of 5 Reps: ITW=BSC×(−0.18 ln(TR)+1.0212) ITW=(15/0.5338×(−0.18 ln(5)+1.0212) ITW=28.103×0.7315=20.557 lbs.

To perform the first set of first set of 5 Dumbbell Curl's, the user would ideally use a weight of 20.557 Lbs. As weights don't come in such minor weight increments, this ITW will be adjusted, as explained further in this example.

The Ideal Target Weight (ITW) is determined for Exercise 1 Set 3 which has a Target Repetition (TR) of 15 Reps: ITW=BSC×(−0.18 ln(TR)+1.0212) ITW=(15/0.5338×(−0.18 ln(15)+1.0212) ITW=28.103×0.5338=13.545 lbs.

To perform the first set of first set of 15 Dumbbell Curl's, the user would ideally use a weight of 13.545 Lbs. As weights don't come in such minor weight increments, this ITW will be adjusted, as explained further in this example.

The Ideal Target Weight (ITW) is determined for Exercise 1 Set 4 which has a Target Repetition (TR) of 10 Reps: ITW=BSC×(−0.18 ln(TR)+1.0212) ITW=(15/0.5338×(−0.18 ln(10)+1.0212) ITW=28.103×0.6067=17.051 lbs.

To perform the first set of first set of 10 Dumbbell Curl's, the user would ideally use a weight of 17.051 Lbs. As weights don't come in such minor weight increments, this ITW will be adjusted, as explained further in this example.

The user may then choose a weight to use: Available Weight Used (AWU). Gyms do not have weights or resistance equipment to match the Ideal Target Weight (ITW) which is calculated down to decimals of a lb. Therefore, the user should choose the next higher weight available and record it as the Available Weight Used (AWU) when reporting the results of each set performed.

Dumbbell Curl ITW AWU Set 1, 20 Target Reps: 12.416 12 Set 2, 5 Target Reps: 20.557 20 Set 3, 15 Target Reps: 13.545 15 Set 4, 10 Target Reps: 17.051 15

Whenever significant variations in a user group's performance differentiates from other user groups, the system 100 and method 1400 creates a new specific BSCA's for that specific group.

An exemplary workout routine involves three workouts per week and five exercises per workout. A week 1 and week 2 of weightlifting exercises has the same exercises but different repetition targets. Week 1 target reps 20, 5, 15 and 10 Reps. And week 2 target reps for the same exercises but with 10, 15, 20, and 25 reps.

The first time the user performs a new exercise he/she must do one set of reps of that exercise with a weight he/she figures they can complete between 5 and 15 reps before the muscle reaches total fatigue until the next repetition cannot be completed. After completing the set the user registers the weight (W) used and the Repetitions completed (CR). The program calculates the Coefficient datapoint on the Baseline Strength Curve using the Baseline Strength Curve Algorithm appropriate for the User's Profile and the Baseline Strength Value for that exercise.

The repetition targets of 20, 5, 15 and 10 for week 1 are entered. The repetition targets for week 2 of 10, 15, 20, and 25 are entered. Based on the above information the system calculates the Ideal Target Weight (ITW) for each of the sets, given their respective target repetitions (TR). Based on the weights or resistance available at the user's gym, he/she inputs the Available Weight Used (AWU) as near to the system's generated ITW and begins performing the exercises in the sequence prescribed by his/her workout plan with that weight.

The user then performs as many repetitions as possible until reaching total muscle fatigue for set 1, then inputs the number of repetitions completed as CR. After a prescribed west period of 2 to 3 minutes he/she repeats the same process for sets 2, 3 and 4 and inputs the number of reps achieved. This workout plan calls for the same exercises be performed the following week, but with sets with different target repetitions (TR) as mentioned above—10, 15, 20, and 25.

Prior to the person starting the second week of exercises, the system updates the BSV, with the result of the previous week's workout. This uses the strength level achieved in the first week's workout as a baseline for the second week's workout. If the person exceeds the number of Ideal Target Weight (ITW) with the new value of Available Weight Used (AWU), the system will automatically register this as an increase in the BSV and increase the ITW weight for the next training session. If the person underperforms, the BSV will decrease and the system will adjust the ITW down for the next training session. The user can follow his or her strength progression referring to the Total Training values in columns BE and BF, which show the amount of weight “moved” in performing the specific exercise on that training day.

The Baseline Strength Coefficient and Baseline Strength Curve Algorithm is personalized. Every individual has a different physiologic composition, age, sex, training background, history of injuries and an infinite number of other differences which differentiates him/her from all the other people in the world. These differences may be substantial enough to cause his/her strength curve profile to diverge from the Baseline Strength Curve Algorithm initially used by the system based on the provided User Profile.

In the case a persons Achieved Strength Coefficients for any given exercise deviate over time from the Baseline Strength Coefficient, the system will identify this abnormality and proceed to test and adjust the individual's performance data and create an Individual Users Strength Coefficient for the user. This is done for the full range of repetitions performed in executing the given exercise. The coefficient is adjusted, and from the resulting new strength coefficients in different rep values, an Individual Users Algorithm for the user and the specific exercise is generated. This is used as the algorithm to project the Baseline Strength Coefficient. which in turn generates the Ideal Target Weight for the user when performing the that specific exercise with a given number of Target Repetitions. The personalized strength curve evolves and changes form as the user's strength changes. The curve automatically adapts to the user's strength progression and muscle development.

Various modifications and additions can be made to the exemplary embodiments discussed without departing from the scope of the present disclosure. For example, while the embodiments described above refer to particular features, the scope of this disclosure also includes embodiments having different combinations of features and embodiments that do not include all of the above described features. 

What is claimed is:
 1. A computer-implemented and resistance-implemented method for recommending an ideal number of repetitions and an ideal target resistance value for an exercise routine, the method comprising the steps: providing at least one resistance structure with a resistance value and operably configured to be manipulated by a user for an exercise routine; providing a mobile electronic device of the user with a software application resident thereon, the mobile electronic device operably configured to communicatively couple with a server computer over a network, the mobile electronic device of the user and the server computer each having a processor and memory operably coupled thereto; manipulating the at least one resistance structure by the user until a muscle fatigue is reached employing a recommended proper resistance structure use protocol for the exercise routine and ascertaining a number of initial completed repetitions associated with the resistance value; initiating the software application to generate a user interface on the mobile electronic device; utilizing the number of initial completed repetitions on the user interface of the software application to generate, with one of a plurality of user-data defined y-intercept approximation functions, a baseline strength coefficient, the resistance value divided by the baseline strength coefficient to generate a baseline strength value; utilizing the baseline strength value on the user interface of the software application to generate a plurality of ideal target weight values utilizing the one of the plurality of user-data defined y-intercept approximation functions, each involving a plurality of user-selected desired target repetition values; and manipulating by the user, during a plurality of subsequent sets involving the recommended proper resistance structure use protocol for the exercise routine, the at least one resistance structure associated with the plurality of ideal target weight values, respectively, that are each rounded to the nearest whole number and for the greater of the plurality of user-selected desired target repetition values or until a muscle fatigue is reached to generate second completed repetition values for each of the plurality of subsequent sets.
 2. The computer-implemented and resistance-implemented method according to claim 1, further comprising: plotting the rounded plurality of ideal target weight values with respect to the second completed repetition values for each of the plurality of subsequent sets to generate another of the plurality of user-data defined y-intercept approximation functions dictated by a highest coefficient of determination.
 3. The computer-implemented and resistance-implemented method according to claim 2, wherein the another of the plurality of user-data defined y-intercept approximation functions consist essentially of: a logarithmic function, a quadratic function, and a power function.
 4. The computer-implemented and resistance-implemented method according to claim 3, wherein: the one of a plurality of y-intercept approximation functions is a natural logarithmic function.
 5. The computer-implemented and resistance-implemented method according to claim 4, wherein: the natural logarithmic function is xln(ICR)+b, wherein ICR is the number of initial completed repetitions associated with the resistance value.
 6. The computer-implemented and resistance-implemented method according to claim 1, wherein the resistance structure further comprises: at least one free-weight structure with the resistance value and operably configured to be lifted by a user for the exercise routine.
 7. The computer-implemented and resistance-implemented method according to claim 6, wherein: the recommended proper resistance structure use protocol comprises a bicep curl motion using a dumbbell for an arm curl, or a 90-degree extension motion for a leg curl.
 8. The computer-implemented and resistance-implemented method according to claim 1, wherein: the number of initial completed repetitions associated with the resistance value comprises 5 to 20 repetitions.
 9. The computer-implemented and resistance-implemented method according to claim 1, further comprising: utilizing the number of initial completed repetitions, and the resistance value of the at least one resistance structure, on the user interface of the software application to generate, with one of a plurality of user-data defined y-intercept approximation functions, a baseline strength coefficient, the resistance value divided by the baseline strength coefficient to generate a baseline strength value.
 10. The computer-implemented and resistance-implemented method according to claim 9, further comprising: generating a strength curve based on the completed repetitions, and the resistance value of the resistance structure, whereby the repetitions comprise a user-defined x-intercept variable, and the weight values comprise a user-data defined y-intercept variable.
 11. The computer-implemented and resistance-implemented method according to claim 1, wherein: the baseline strength coefficient is generated on the user interface of the software application with at least one of a look-up table and a completed repetition input field.
 12. A computer-implemented and resistance-implemented method for recommending an ideal number of repetitions and an ideal target resistance value for a weight-training routine, the method comprising the steps: providing at least one resistance structure with a resistance value and operably configured to be manipulated by a user for an exercise routine; providing a mobile electronic device of the user with a software application resident thereon, the mobile electronic device operably configured to communicatively couple with a server computer over a network, the mobile electronic device of the user and the server computer each having a processor and memory operably coupled thereto; manipulating the at least one resistance structure by the user until a muscle fatigue is reached employing a recommended proper resistance use protocol for the exercise routine and ascertaining a number of initial completed repetitions associated with the resistance value; generating a strength curve based on the completed repetitions, and the resistance value of the resistance structure, whereby the repetitions comprise a user-defined x-intercept variable, and the weight values comprise a user-data defined y-intercept variable; initiating the software application to generate a user interface on the mobile electronic device; utilizing the number of initial completed repetitions on the user interface of the software application to generate, with one of a plurality of user-data defined y-intercept approximation functions, a baseline strength coefficient, the resistance value divided by the baseline strength coefficient to generate a baseline strength value; utilizing the baseline strength value on the user interface of the software application to generate a plurality of ideal target resistance values utilizing the one of the plurality of user-data defined y-intercept approximation functions, each involving a plurality of user-selected desired target repetition values; manipulating by the user, during a plurality of subsequent sets involving the recommended proper resistance structure use protocol for the exercise routine, the at least one resistance structure associated with the plurality of ideal target weight values, respectively, that are each rounded to the nearest whole number and for the greater of the plurality of user-selected desired target repetition values or until a muscle fatigue is reached to generate second completed repetition values for each of the plurality of subsequent sets; and plotting the rounded plurality of ideal target weight values with respect to the second completed repetition values for each of the plurality of subsequent sets to generate another of the plurality of user-data defined y-intercept approximation functions dictated by a highest coefficient of determination.
 13. The computer-implemented and resistance-implemented method according to claim 12, wherein the another of the plurality of user-data defined y-intercept approximation functions consist essentially of: a logarithmic function, a quadratic function, and a power function.
 14. The computer-implemented and resistance-implemented method according to claim 13, wherein: the one of a plurality of y-intercept approximation functions is a natural logarithmic function.
 15. The computer-implemented and resistance-implemented method according to claim 14, wherein: the natural logarithmic function is xln(ICR)+b, wherein ICR is the number of initial completed repetitions associated with the weight value.
 16. The computer-implemented and resistance-implemented method according to claim 15, wherein: the baseline strength coefficient is generated on the user interface of the software application with at least one of a look-up table and a completed repetition input field.
 17. A system for recommending an ideal number of repetitions and an ideal target weight for a weight-training routine, the system comprising: at least one free weight structure with a weight or resistance value and operably configured to be lifted by a user for an exercise routine; a mobile electronic device of the user with a software application resident thereon, the mobile electronic device operably configured to communicatively couple with a server computer over a network, the mobile electronic device of the user and the server computer each having a processor and memory operably coupled thereto; whereby the at least one free weight structure is lifted by the user until a muscle fatigue is reached employing a recommended proper weight use protocol for the exercise routine and ascertaining a number of initial completed repetitions associated with the weight value; a strength curve based on the completed repetitions, and the weight or resistance value of the free weight structure, whereby the repetitions comprise a user-defined x-intercept variable, and the weight values comprise a user-data defined y-intercept variable; a user interface generated on the mobile electronic device; a plurality of user-data defined y-intercept approximation functions generating the number of initial completed repetitions on the user interface of the software application, a baseline strength coefficient, the weight or resistance value divided by the baseline strength coefficient to generate a baseline strength value; a plurality of ideal target weight values generated by the baseline strength value on the user interface of the software application, the ideal target weight values utilizing the one of the plurality of user-data defined y-intercept approximation functions, each involving a plurality of user-selected desired target repetition values; whereby the user lifts, during a plurality of subsequent sets involving the recommended proper weight use protocol for the exercise routine, the at least one free weight structure associated with the plurality of ideal target weight values, respectively, that are each rounded to the nearest whole number and for the greater of the plurality of user-selected desired target repetition values or until a muscle fatigue is reached to generate second completed repetition values for each of the plurality of subsequent sets; and another of the plurality of user-data defined y-intercept approximation functions dictated by a highest coefficient of determination generated by plotting the rounded plurality of ideal target weight values with respect to the second completed repetition values for each of the plurality of subsequent sets to generate. 